ABSTRACT
Parkinson's disease is a progressive, neurodegenerative disorder, characterized by hallmark motor symptoms. It is associated with pathological, oscillatory neural activity in the basal ganglia. Deep brain stimulation (DBS) is often successfully used to treat medically refractive Parkinson's disease. However, the selection of stimulation parameters is based on qualitative assessment of the patient, which can result in a lengthy tuning period and a suboptimal choice of parameters. This study explores fourth-order, control theory-based models of oscillatory activity in the basal ganglia. Describing function analysis is applied to examine possible mechanisms for the generation of oscillations in interacting nuclei and to investigate the suppression of oscillations with high-frequency stimulation. The theoretical results for the suppression of the oscillatory activity obtained using both the fourth-order model, and a previously described second-order model, are optimized to fit clinically recorded local field potential data obtained from Parkinsonian patients with implanted DBS. Close agreement between the power of oscillations recorded for a range of stimulation amplitudes is observed ( R(2)=0.69-0.99 ). The results suggest that the behavior of the system and the suppression of pathological neural oscillations with DBS is well described by the macroscopic models presented. The results also demonstrate that in this instance, a second-order model is sufficient to model the clinical data, without the need for added complexity. Describing the system behavior with computationally efficient models could aid in the identification of optimal stimulation parameters for patients in a clinical environment.
Subject(s)
Basal Ganglia/physiopathology , Deep Brain Stimulation/methods , Models, Neurological , Parkinson Disease/physiopathology , Humans , Parkinson Disease/therapyABSTRACT
Deep brain stimulation effectively alleviates motor symptoms of medically refractory Parkinson's disease, and also relieves many other treatment-resistant movement and affective disorders. Despite its relative success as a treatment option, the basis of its efficacy remains elusive. In Parkinson's disease, increased functional connectivity and oscillatory activity occur within the basal ganglia as a result of dopamine loss. A correlative relationship between pathological oscillatory activity and the motor symptoms of the disease, in particular bradykinesia, rigidity, and tremor, has been established. Suppression of the oscillations by either dopamine replacement or DBS also correlates with an improvement in motor symptoms. DBS parameters are currently chosen empirically using a "trial and error" approach, which can be time-consuming and costly. The work presented here amalgamates concepts from theories of neural network modeling with nonlinear control engineering to describe and analyze a model of synchronous neural activity and applied stimulation. A theoretical expression for the optimum stimulation parameters necessary to suppress oscillations is derived. The effect of changing stimulation parameters (amplitude and pulse duration) on induced oscillations is studied in the model. Increasing either stimulation pulse duration or amplitude enhanced the level of suppression. The predicted parameters were found to agree well with clinical measurements reported in the literature for individual patients. It is anticipated that the simplified model described may facilitate the development of protocols to aid optimum stimulation parameter choice on a patient by patient basis.
Subject(s)
Basal Ganglia/physiology , Basal Ganglia/physiopathology , Deep Brain Stimulation/methods , Models, Neurological , Computer Simulation , Databases, Factual , Humans , Parkinson Disease/physiopathology , Signal Processing, Computer-AssistedABSTRACT
Deep brain stimulation (DBS) effectively alleviates the pathological neural activity associated with Parkinson's disease. Its exact mode of action is not entirely understood. This paper explores theoretically the optimum stimulation parameters necessary to quench oscillations in a neural-mass type model with second order dynamics. This model applies well established nonlinear control system theory to DBS. The analysis here determines the minimum criteria in terms of amplitude and pulse duration of stimulation, necessary to quench the unwanted oscillations in a closed loop system, and outlines the relationship between this model and the actual physiological system.
Subject(s)
Deep Brain Stimulation/methods , Parkinson Disease/therapy , Algorithms , Basal Ganglia/pathology , Feedback , Globus Pallidus/pathology , Humans , Linear Models , Models, Statistical , Neurons/pathology , Nonlinear Dynamics , Oscillometry/methods , Parkinson Disease/physiopathology , Reproducibility of ResultsABSTRACT
Deep brain stimulation (DBS) is a widely applied clinical procedure for the alleviation of pathological neural activity, and is particularly effective in suppressing the symptoms of Parkinson's disease. The mechanisms of action of DBS remain to be fully elucidated. In this paper, we present an application to DBS of the concepts of dither injection and equivalent nonlinearity from the theory of nonlinear feedback control systems. We propose that this model provides a framework for understanding the mechanism by which an injected high-frequency signal can quench undesired oscillations in closed-loop systems of interacting neurons in the brain.
